OpenRaiser/Intern-Atlas huggingface dataset:
Fast Rigid 3D Registration Solution: A Simple Method Free of SVD and Eigen-Decomposition
Venue: arXiv cs.SY 2018
Year: 2018

A novel solution is obtained to solve the rigid 3D registration problem, motivated by previous eigen-decomposition approaches. Different from existing solvers, the proposed algorithm does not require sophisticated matrix operations e.g. singular value decomposition or eigenvalue decomposition. Instead, the optimal eigenvector of the point cross-covariance matrix can be computed within several iterations. It is also proven that the optimal rotation matrix can be directly computed for cases without need of quaternion. The simple framework provides very easy approach of integer-implementation on embedded platforms. Simulations on noise-corrupted point clouds have verified the robustness and computation speed of the proposed method. The final results indicate that the proposed algorithm is accurate, robust and owns over $60\% \sim 80\%$ less computation time than representatives. It has also been applied to real-world applications for faster relative robotic navigation.
Capacity of Single-Server Single-Message Private Information Retrieval with Coded Side Information
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers the problem of single-server single-message private information retrieval with coded side information (PIR-CSI). In this problem, there is a server storing a database, and a user which knows a linear combination of a subset of messages in the database as a side information. The number of messages contributing to the side information is known to the server, but the indices and the coefficients of these messages are unknown to the server. The user wishes to download a message from the server privately, i.e., without revealing which message it is requesting, while minimizing the download cost. In this work, we consider two different settings for the PIR-CSI problem depending on the demanded message being or not being one of the messages contributing to the side information. For each setting, we prove an upper bound on the maximum download rate as a function of the size of the database and the size of the side information, and propose a protocol that achieves the rate upper-bound.
A Stochastic Geometry Analysis of Energy Harvesting in Large Scale Wireless Networks
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, the theoretical sustainable capacity of wireless networks with radio frequency (RF) energy harvesting is analytically studied. Specifically, we consider a large scale wireless network where base stations (BSs) and low power wireless devices are deployed by homogeneous Poisson point process (PPP) with different spatial densities. Wireless devices exploit the downlink transmissions from the BSs for either information delivery or energy harvesting. Generally, a BS schedules downlink transmission to wireless devices. The scheduled device receives the data information while other devices harvest energy from the downlink signals. The data information can be successfully received by the scheduled device only if the device has sufficient energy for data processing, i.e., the harvested energy is larger than a threshold. Given the densities of BSs and users, we apply stochastic geometry to analyze the expected number of users per cell and the successful information delivery probability of a wireless device, based on which the total network throughput can be derived. It is shown that the maximum network throughput per cell can be achieved under the optimal density of BSs. Extensive simulations validate the analysis.
Hybrid Data-Sharing and Compression Strategy for Downlink Cloud Radio Access Network
Venue: arXiv cs.IT 2018
Year: 2018

This paper studies transmission strategies for the downlink of a cloud radio access network, in which the base stations are connected to a centralized cloud-computing based processor with digital fronthaul or backhaul links. We provide a system-level performance comparison of two fundamentally different strategies, namely the data-sharing strategy and the compression strategy, that differ in the way the fronthaul/backhaul is utilized. It is observed that the performance of both strategies depends crucially on the available fronthaul or backhaul capacity. When the fronthaul/backhaul capacity is low, the datasharing strategy performs better, while under moderate-to-high fronthaul/backhaul capacity, the compression strategy is superior. Using insights from such a comparison, we propose a novel hybrid strategy, combining the data-sharing and compression strategies, that allows for better control over the fronthaul/backhaul capacity utilization. An optimization framework for the hybrid strategy is proposed. Numerical evidence demonstrates the performance gain of the hybrid strategy.
Synthesis methods for reversible circuits consisting of NOT, CNOT and 2-CNOT gates (Ph.D. thesis)
Venue: arXiv cs.ET 2018
Year: 2018

In this paper, reversible circuits consisting of NOT, CNOT and 2-CNOT gates are studied. Several asymptotically optimal by the order of magnitude synthesis methods are described. Some circuit's complexity reduction approaches are considered. Implementation of discrete logarithm within a reversible circuit is discussed. The main conclusion in the paper is that the usage of additional inputs (additional memory) in reversible circuits almost always allow to reduce their complexity, depth and weight.
Second-Order Asymptotically Optimal Statistical Classification
Venue: arXiv cs.IT 2018
Year: 2018

Motivated by real-world machine learning applications, we analyze approximations to the non-asymptotic fundamental limits of statistical classification. In the binary version of this problem, given two training sequences generated according to two {\em unknown} distributions $P_1$ and $P_2$, one is tasked to classify a test sequence which is known to be generated according to either $P_1$ or $P_2$. This problem can be thought of as an analogue of the binary hypothesis testing problem but in the present setting, the generating distributions are unknown. Due to finite sample considerations, we consider the second-order asymptotics (or dispersion-type) tradeoff between type-I and type-II error probabilities for tests which ensure that (i) the type-I error probability for {\em all} pairs of distributions decays exponentially fast and (ii) the type-II error probability for a {\em particular} pair of distributions is non-vanishing. We generalize our results to classification of multiple hypotheses with the rejection option.
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network
Venue: arXiv cs.CV 2018
Year: 2018

Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.
Location Privacy in Cognitive Radio Networks: A Survey
Venue: arXiv cs.NI 2018
Year: 2018

Cognitive radio networks (CRNs) have emerged as an essential technology to enable dynamic and opportunistic spectrum access which aims to exploit underutilized licensed channels to solve the spectrum scarcity problem. Despite the great benefits that CRNs offer in terms of their ability to improve spectrum utilization efficiency, they suffer from user location privacy issues. Knowing that their whereabouts may be exposed can discourage users from joining and participating in the CRNs, thereby potentially hindering the adoption and deployment of this technology in future generation networks. The location information leakage issue in the CRN context has recently started to gain attention from the research community due to its importance, and several research efforts have been made to tackle it. However, to the best of our knowledge, none of these works have tried to identify the vulnerabilities that are behind this issue or discuss the approaches that could be deployed to prevent it. In this paper, we try to fill this gap by providing a comprehensive survey that investigates the various location privacy risks and threats that may arise from the different components of this CRN technology, and explores the different privacy attacks and countermeasure solutions that have been proposed in the literature to cope with this location privacy issue. We also discuss some open research problems, related to this issue, that need to be overcome by the research community to take advantage of the benefits of this key CRN technology without having to sacrifice the users' privacy.
Psychological State in Text: A Limitation of Sentiment Analysis
Venue: arXiv cs.CL 2018
Year: 2018

Starting with the idea that sentiment analysis models should be able to predict not only positive or negative but also other psychological states of a person, we implement a sentiment analysis model to investigate the relationship between the model and emotional state. We first examine psychological measurements of 64 participants and ask them to write a book report about a story. After that, we train our sentiment analysis model using crawled movie review data. We finally evaluate participants' writings, using the pretrained model as a concept of transfer learning. The result shows that sentiment analysis model performs good at predicting a score, but the score does not have any correlation with human's self-checked sentiment.
Novel Compliant omnicrawler-wheel transforming module
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents a novel design of a crawler robot which is capable of transforming its chassis from an Omni crawler mode to a large-sized wheel mode using a novel mechanism. The transformation occurs without any additional actuators. Interestingly the robot can transform into a large diameter and small width wheel which enhances its maneuverability like small turning radius and fast/efficient locomotion. This paper contributes on improving the locomotion mode of previously developed hybrid compliant omnicrawler robot CObRaSO. In addition to legged and tracked mechanism, CObRaSO can now display large wheel mode which contributes to its locomotion capabilities. Mechanical design of the robot has been explained in a detailed manner in this paper and also the transforming experiment and torque analysis has been shown clearly
Echo state networks are universal
Venue: arXiv cs.NE 2018
Year: 2018

This paper shows that echo state networks are universal uniform approximants in the context of discrete-time fading memory filters with uniformly bounded inputs defined on negative infinite times. This result guarantees that any fading memory input/output system in discrete time can be realized as a simple finite-dimensional neural network-type state-space model with a static linear readout map. This approximation is valid for infinite time intervals. The proof of this statement is based on fundamental results, also presented in this work, about the topological nature of the fading memory property and about reservoir computing systems generated by continuous reservoir maps.
A New Style of Proof for Mathematics Organized as a Network of Axiomatic Theories
Venue: arXiv cs.LO 2018
Year: 2018

A theory graph is a network of axiomatic theories connected with meaning-preserving mappings called theory morphisms. Theory graphs are well suited for organizing large bodies of mathematical knowledge. Traditional and formal proofs do not adequately fulfill all the purposes that mathematical proofs have, and they do not exploit the structure inherent in a theory graph. We propose a new style of proof that fulfills the principal purposes of a mathematical proof as well as capitalizes on the connections provided by the theory morphisms in a theory graph. This new style of proof combines the strengths of traditional proofs with the strengths of formal proofs.
Studying Politically Vulnerable Communities Online: Ethical Dilemmas, Questions, and Solutions
Venue: arXiv cs.CY 2018
Year: 2018

This short article introduces the concept of political vulnerability for social media researchers. How are traditional notions of harm challenged by research subjects in politically vulnerable communities? Through a selection of case studies, we explore some of the trade-offs, challenges, and questions raised by research that seeks be robust and transparent while also preserving anonymity and privacy, especially in high-stakes, politically fraught contexts.
Measuring Influence on Instagram: a Network-oblivious Approach
Venue: arXiv cs.SI 2018
Year: 2018

This paper focuses on the problem of scoring and ranking influential users of Instagram, a visual content sharing online social network (OSN). Instagram is the second largest OSN in the world with 700 million active Instagram accounts, 32% of all worldwide Internet users. Among the millions of users, photos shared by more influential users are viewed by more users than posts shared by less influential counterparts. This raises the question of how to identify those influential Instagram users. In our work, we present and discuss the lack of relevant tools and insufficient metrics for influence measurement, focusing on a network oblivious approach and show that the graph-based approach used in other OSNs is a poor fit for Instagram. In our study, we consider user statistics, some of which are more intuitive than others, and several regression models to measure users' influence.
PID2018 Benchmark Challenge:Multi-Objective Stochastic Optimization Algorithm
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents a multi-objective stochastic optimization method for tuning of the controller parameters of Refrigeration Systems based on Vapour Compression. Stochastic Multi Parameter Divergence Optimization (SMDO) algorithm is modified for minimization of the Multi Objective function for optimization process. System control performance is improved by tuning of the PI controller parameters according to discrete time model of the refrigeration system with multi objective function by adding conditional integral structure that is preferred to reduce the steady state error of the system. Simulations are compared with existing results via many graphical and numerical solutions.
Player preferences in $N$-player games
Venue: arXiv cs.GT 2018
Year: 2018

In this paper we describe several player preferences in games with $N \geq 2$ players, in particular the case $N = 3$, and use them to simplify game trees, using the game of Clobber as our example. We show that, using a fixed starting player and a certain ruleset, any short game can be simplified to a value in a very concise set. Omitting the fixed starting player and generalising the theory to more than 3 players remains a challenge.
SuMo-SS: Submodular Optimization Sensor Scattering for Deploying Sensor Networks by Drones
Venue: arXiv cs.RO 2018
Year: 2018

To meet the immediate needs of environmental monitoring or hazardous event detection, we consider the automatic deployment of a group of low-cost or disposable sensors by a drone. Introducing sensors by drones to an environment instead of humans has advantages in terms of worker safety and time requirements. In this study, we define "sensor scattering (SS)" as the problem of maximizing the information-theoretic gain from sensors scattered on the ground by a drone. SS is challenging due to its combinatorial explosion nature, because the number of possible combination of sensor positions increases exponentially with the increase in the number of sensors. In this paper, we propose an online planning method called SubModular Optimization Sensor Scattering (SuMo-SS). Unlike existing methods, the proposed method can deal with uncertainty in sensor positions. It does not suffer from combinatorial explosion but obtains a (1-1/e)-approximation of the optimal solution. We built a physical drone that can scatter sensors in an indoor environment as well as a simulation environment based on the drone and the environment. In this paper, we present the theoretical background of our proposed method and its experimental validation.
Sloth Search System at the Video Browser Showdown 2018 - Final Notes
Venue: arXiv cs.MM 2018
Year: 2018

This short paper provides further details of the Sloth Search System, which was developed by the NECTEC team for the Video Browser Showdown (VBS) 2018.
Modeling Realistic Degradations in Non-blind Deconvolution
Venue: arXiv cs.CV 2018
Year: 2018

Most image deblurring methods assume an over-simplistic image formation model and as a result are sensitive to more realistic image degradations. We propose a novel variational framework, that explicitly handles pixel saturation, noise, quantization, as well as non-linear camera response function due to e.g., gamma correction. We show that accurately modeling a more realistic image acquisition pipeline leads to significant improvements, both in terms of image quality and PSNR. Furthermore, we show that incorporating the non-linear response in both the data and the regularization terms of the proposed energy leads to a more detailed restoration than a naive inversion of the non-linear curve. The minimization of the proposed energy is performed using stochastic optimization. A dataset consisting of realistically degraded images is created in order to evaluate the method.
Consolidating the innovative concepts towards Exascale computing for Co-Design of Co-Applications ll: Co-Design Automation - Workload Characterization
Venue: arXiv cs.DC 2018
Year: 2018

Many-core co-design is a complex task in which application complexity design space, heterogeneous many-core architecture design space, parallel programming language design space, simulator design space and optimizer design space should get integrated through a binding process and these design spaces, an ensemble of what is called many-core co-design spaces. It is indispensable to build a co-design automation process to dominate over the co-design complexity to cut down the turnaround time. The co-design automation is frame worked to comprehend the dependencies across the many-core co-design spaces and devise the logic behind these interdependencies using a set of algorithms. The software modules of these algorithms and the rest from the many-core co-design spaces interact to crop up the power-performance optimized heterogeneous many-core architecture specific for the simultaneous execution of co applications without space-time sharing. It is essential that such co-design automation has a built-in user-customizable workload generator to benchmark the emerging many-core architecture. This customizability benefits the generation of complex workloads with the desired computation complexity, communication complexity, control flow complexity, and locality of reference, specified under a distribution and established on quantitative models. In addition, the customizable workload model aids the generation of what is called computational and communication surges. None of the current day benchmark suites encompasses applications and kernels that can match the attributes of customizable workload model proposed in this paper. Aforementioned concepts are exemplified in, the case study supported by simulation results gathered from the simulator.
Performance tuning for deep learning on a many-core processor (master thesis)
Venue: arXiv cs.DC 2018
Year: 2018

Convolutional neural networks (CNNs) are becoming very successful and popular for a variety of applications. The Loki many-core processor architecture is very promising for achieving specialised hardware performance and efficiency while being a general purpose solution. Loki combines many simple cores with increased control for the programmer. This freedom can be exploited to produce much more efficient code than in conventional multiprocessors but it also creates a very big design space for possible optimisations. In this project, I explore possible optimisations for a CNN application, their portability on different Loki-specific configurations, convolution parameters and inputs. Finally, I investigate the potential for adaptive algorithms for further performance increase.
Spark-MPI: Approaching the Fifth Paradigm of Cognitive Applications
Venue: arXiv cs.DC 2018
Year: 2018

Over the past decade, the fourth paradigm of data-intensive science rapidly became a major driving concept of multiple application domains encompassing and generating large-scale devices such as light sources and cutting edge telescopes. The success of data-intensive projects subsequently triggered the next generation of machine learning approaches. These new artificial intelligent systems clearly represent a paradigm shift from data processing pipelines towards the fifth paradigm of composite cognitive applications requiring the integration of Big Data processing platforms and HPC technologies. The paper addresses the existing impedance mismatch between data-intensive and compute-intensive ecosystems by presenting the Spark-MPI approach based on the MPI Exascale Process Management Interface (PMIx). The approach is demonstrated within the context of hybrid MPI/GPU ptychographic image reconstruction pipelines and distributed deep learning applications.
Covering with Clubs: Complexity and Approximability
Venue: arXiv cs.DS 2018
Year: 2018

Finding cohesive subgraphs in a network is a well-known problem in graph theory. Several alternative formulations of cohesive subgraph have been proposed, a notable example being $s$-club, which is a subgraph where each vertex is at distance at most $s$ to the others. Here we consider the problem of covering a given graph with the minimum number of $s$-clubs. We study the computational and approximation complexity of this problem, when $s$ is equal to 2 or 3. First, we show that deciding if there exists a cover of a graph with three $2$-clubs is NP-complete, and that deciding if there exists a cover of a graph with two $3$-clubs is NP-complete. Then, we consider the approximation complexity of covering a graph with the minimum number of $2$-clubs and $3$-clubs. We show that, given a graph $G=(V,E)$ to be covered, covering $G$ with the minimum number of $2$-clubs is not approximable within factor $O(|V|^{1/2 -\varepsilon})$, for any $\varepsilon>0$, and covering $G$ with the minimum number of $3$-clubs is not approximable within factor $O(|V|^{1 -\varepsilon})$, for any $\varepsilon>0$. On the positive side, we give an approximation algorithm of factor $2|V|^{1/2}\log^{3/2} |V|$ for covering a graph with the minimum number of $2$-clubs.